3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle tracking in unsupervised 2D SOT, we introduce the first semi-supervised approach to 3D SOT. Specifically, we introduce two cycle-consistency strategies for supervision: 1) Self tracking cycles, which leverage labels to help the model converge better in the early stages of training; 2) forward-backward cycles, which strengthen the tracker's robustness to motion variations and the template noise caused by the template update strategy. Furthermore, we propose a data augmentation strategy named SOTMixup to improve the tracker's robustness to point cloud diversity. SOTMixup generates training samples by sampling points in two point clouds with a mixing rate and assigns a reasonable loss weight for training according to the mixing rate. The resulting MixCycle approach generalizes to appearance matching-based trackers. On the KITTI benchmark, based on the P2B tracker, MixCycle trained with $\textbf{10\%}$ labels outperforms P2B trained with $\textbf{100\%}$ labels, and achieves a $\textbf{28.4\%}$ precision improvement when using $\textbf{1\%}$ labels. Our code will be released at \url{https://github.com/Mumuqiao/MixCycle}.

翻译：3D单目标跟踪是自动驾驶不可或缺的组成部分。现有方法严重依赖大规模密集标注数据集，然而点云标注既昂贵又耗时。受无监督2D单目标跟踪中循环跟踪巨大成功的启发，我们首次提出针对3D单目标跟踪的半监督方法。具体而言，我们引入两种用于监督的循环一致性策略：1）自跟踪循环，利用标签帮助模型在训练早期阶段更好地收敛；2）前向-后向循环，增强跟踪器对运动变化以及由模板更新策略引起的模板噪声的鲁棒性。此外，我们提出名为SOTMixup的数据增强策略，以提升跟踪器对点云多样性的鲁棒性。SOTMixup通过混合比率在两个点云中采样点来生成训练样本，并根据混合比率为训练分配合理的损失权重。所提出的MixCycle方法可泛化至基于外观匹配的跟踪器。在KITTI基准上，基于P2B跟踪器，使用$\textbf{10\%}$标签训练的MixCycle优于使用$\textbf{100\%}$标签训练的P2B，且在使用$\textbf{1\%}$标签时实现了$\textbf{28.4\%}$的精度提升。我们的代码将发布在\url{https://github.com/Mumuqiao/MixCycle}。